Data-driven Bayesian Network structure learning for probabilistic inference of rainfall in Rio de Janeiro

Abstract

Abstract Bayesian Networks (BNs) provide an interpretable probabilistic framework for modeling uncertainty in complex environmental systems. This study proposes a data-driven Bayesian Network (BN) to represent probabilistic relationships among meteorological variables and precipitation in Rio de Janeiro, Brazil, using hourly data from telemetric stations (2002–2024). The methodology combines physically motivated feature engineering, PCA-based assessment of redundant temporal-variation features, and structure learning under meteorology-informed constraints to ensure temporal and physical coherence. Structural constraints based on meteorological knowledge ensured temporal and physical coherence. Among the evaluated models, the Hill-Climbing algorithm with the K2 criterion achieved the best predictive performance in terms of accuracy, specificity and calibration. However, for interpretative analysis, the Hill-Climbing algorithm with the Bayesian Information Criterion (BIC) criterion was selected due to its more parsimonious structure, which aligns better with physical reasoning. The learned structure indicated that rainfall depends mainly on short-term atmospheric conditions, particularly high humidity, low solar radiation, and moderate wind speeds at one-hour lags. Validation confirmed consistent cross-validation results and physically coherent patterns associated with convection and moisture transport. By explicitly representing conditional dependencies, the proposed BN improves interpretability over black-box methods and supports probabilistic reasoning for short-term precipitation assessment. The findings highlight the potential of BNs as transparent and physically consistent tools for weather modeling and environmental decision support. Furthermore, BNs proved to be a sustainable modeling approach, characterized by low computational demand and minimal energy consumption during the training phase.